Data preprocessing

List of Regions

List of sectors

we will start by adding the symbols of the regions to the nodes

data<-original_data
for (i in 1:12){
  start=(i-1)*20+1
  end=i*20
  for(j in start:end){
    names(data)[j+1] <- paste(str_remove(names(data)[j+1],"\\.[0-9]*$"),paste("_R",i,sep=""),sep="") 
  }
}
data

NA

Naming investement demand as INV_D

  start_inv_d=12*20+2
  end_inv_d=start_inv_d+11
  i=1
  for(j in start_inv_d:end_inv_d){
    names(data)[j] <- paste("INV_D_R",i,sep="")
    i=i+1
  }

Naming Household demand as HO_D

  start_ho_d=end_inv_d+1
  end_ho_d=start_ho_d+11
  i=1
  for(j in start_ho_d:end_ho_d){
    names(data)[j] <- paste("HO_D_R",i,sep="")
    i=i+1
  }

Naming Governoment demand as GOV_D

  start_gov_d=end_ho_d+1
  end_gov_d=start_gov_d+11
  i=1
  for(j in start_gov_d:end_gov_d){
    names(data)[j] <- paste("GOV_D_R",i,sep="")
    i=i+1
  }
  

Naming Foreign exports as FO_EXP

end_for_exp<-ncol(data)
names(data)[end_for_exp] <- "FO_EXP"

data

The purpose of this section is to make the data frame squared for graph construction

Adding the values added

end=12*20+1

squared_data=data.frame(data[,2:end],stringsAsFactors=FALSE)
squared_data

We will create theses duplicates so that if we want to re-run the cell code we’ll not have to run the entire code from the bigenning (note that the data changes)

squared_data_dup=squared_data

Here we want to make the matrix squared by adding zeros

Adding foreign investement

squared_data=squared_data_dup
start=ncol(squared_data)+1
end=start+19
for (i in start:end){
  squared_data=cbind(squared_data,colname=as.factor(numeric(nrow(squared_data))))
  names(squared_data)[ncol(squared_data)] <- paste(data[i,1],"_FOR",sep="")
  
}

squared_data_dup2=squared_data

Naming and adding taxes and subsidies, labor payments, capital payments, other costs, value added (resp.) as TAX_SUB, LA_PAY, CA_PAY, OTH_COT, VA_ADD

squared_data=squared_data_dup2
for (i in 1:5){
  squared_data=cbind(squared_data,colname=numeric(nrow(squared_data)))
  switch(i,
         names(squared_data)[ncol(squared_data)] <- "TAX_SUB",
         names(squared_data)[ncol(squared_data)] <- "LA_PAY",
         names(squared_data)[ncol(squared_data)] <- "CA_PAY",
         names(squared_data)[ncol(squared_data)] <- "OTH_COT",
         names(squared_data)[ncol(squared_data)] <- "VA_ADD",
         )
}

squared_data

squared_data_dup3=squared_data

squared_data=squared_data_dup3
squared_data=cbind(squared_data,data[,start_inv_d:end_for_exp])

squared_data

n=302-265
m_zeros=matrix(data=0,nrow=n,ncol = ncol(squared_data))
squared_data=data.frame(rbind(as.matrix(squared_data), as.matrix(m_zeros)))
squared_data

Now we will fill the matrix with the actual values of the added columns.

First we will need to convert the data to numeric in order to avoid any casting/levels problems.

Converting data to numeric

squared_data[] <- lapply(squared_data, function(x) {
    if(is.factor(x)) as.numeric(as.character(x)) else x
})
sapply(squared_data, class)

   A00_R1    B05_R1    C00_R1    D01_R1    D02_R1    D03_R1    D04_R1 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
   D05_R1    D06_R1    E00_R1    F45_R1    G00_R1    H55_R1    I01_R1 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
   I02_R1    J00_R1    K00_R1    L75_R1    MNO_R1    OP0_R1    A00_R2 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
   B05_R2    C00_R2    D01_R2    D02_R2    D03_R2    D04_R2    D05_R2 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
   D06_R2    E00_R2    F45_R2    G00_R2    H55_R2    I01_R2    I02_R2 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
   J00_R2    K00_R2    L75_R2    MNO_R2    OP0_R2    A00_R3    B05_R3 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
   C00_R3    D01_R3    D02_R3    D03_R3    D04_R3    D05_R3    D06_R3 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
   E00_R3    F45_R3    G00_R3    H55_R3    I01_R3    I02_R3    J00_R3 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
   K00_R3    L75_R3    MNO_R3    OP0_R3    A00_R4    B05_R4    C00_R4 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
   D01_R4    D02_R4    D03_R4    D04_R4    D05_R4    D06_R4    E00_R4 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
   F45_R4    G00_R4    H55_R4    I01_R4    I02_R4    J00_R4    K00_R4 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
   L75_R4    MNO_R4    OP0_R4    A00_R5    B05_R5    C00_R5    D01_R5 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
   D02_R5    D03_R5    D04_R5    D05_R5    D06_R5    E00_R5    F45_R5 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
   G00_R5    H55_R5    I01_R5    I02_R5    J00_R5    K00_R5    L75_R5 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
   MNO_R5    OP0_R5    A00_R6    B05_R6    C00_R6    D01_R6    D02_R6 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
   D03_R6    D04_R6    D05_R6    D06_R6    E00_R6    F45_R6    G00_R6 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
   H55_R6    I01_R6    I02_R6    J00_R6    K00_R6    L75_R6    MNO_R6 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
   OP0_R6    A00_R7    B05_R7    C00_R7    D01_R7    D02_R7    D03_R7 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
   D04_R7    D05_R7    D06_R7    E00_R7    F45_R7    G00_R7    H55_R7 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
   I01_R7    I02_R7    J00_R7    K00_R7    L75_R7    MNO_R7    OP0_R7 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
   A00_R8    B05_R8    C00_R8    D01_R8    D02_R8    D03_R8    D04_R8 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
   D05_R8    D06_R8    E00_R8    F45_R8    G00_R8    H55_R8    I01_R8 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
   I02_R8    J00_R8    K00_R8    L75_R8    MNO_R8    OP0_R8    A00_R9 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
   B05_R9    C00_R9    D01_R9    D02_R9    D03_R9    D04_R9    D05_R9 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
   D06_R9    E00_R9    F45_R9    G00_R9    H55_R9    I01_R9    I02_R9 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
   J00_R9    K00_R9    L75_R9    MNO_R9    OP0_R9   A00_R10   B05_R10 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
  C00_R10   D01_R10   D02_R10   D03_R10   D04_R10   D05_R10   D06_R10 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
  E00_R10   F45_R10   G00_R10   H55_R10   I01_R10   I02_R10   J00_R10 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
  K00_R10   L75_R10   MNO_R10   OP0_R10   A00_R11   B05_R11   C00_R11 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
  D01_R11   D02_R11   D03_R11   D04_R11   D05_R11   D06_R11   E00_R11 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
  F45_R11   G00_R11   H55_R11   I01_R11   I02_R11   J00_R11   K00_R11 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
  L75_R11   MNO_R11   OP0_R11   A00_R12   B05_R12   C00_R12   D01_R12 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
  D02_R12   D03_R12   D04_R12   D05_R12   D06_R12   E00_R12   F45_R12 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
  G00_R12   H55_R12   I01_R12   I02_R12   J00_R12   K00_R12   L75_R12 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
  MNO_R12   OP0_R12   A00_FOR   B05_FOR   C00_FOR   D01_FOR   D02_FOR 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
  D03_FOR   D04_FOR   D05_FOR   D06_FOR   E00_FOR   F45_FOR   G00_FOR 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
  H55_FOR   I01_FOR   I02_FOR   J00_FOR   K00_FOR   L75_FOR   MNO_FOR 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
  OP0_FOR   TAX_SUB    LA_PAY    CA_PAY   OTH_COT    VA_ADD  INV_D_R1 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
 INV_D_R2  INV_D_R3  INV_D_R4  INV_D_R5  INV_D_R6  INV_D_R7  INV_D_R8 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
 INV_D_R9 INV_D_R10 INV_D_R11 INV_D_R12   HO_D_R1   HO_D_R2   HO_D_R3 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
  HO_D_R4   HO_D_R5   HO_D_R6   HO_D_R7   HO_D_R8   HO_D_R9  HO_D_R10 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
 HO_D_R11  HO_D_R12  GOV_D_R1  GOV_D_R2  GOV_D_R3  GOV_D_R4  GOV_D_R5 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
 GOV_D_R6  GOV_D_R7  GOV_D_R8  GOV_D_R9 GOV_D_R10 GOV_D_R11 GOV_D_R12 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
   FO_EXP 
"numeric" 

squared_data[is.na(squared_data)]=0

squared_data_dup4<-squared_data

Filling the matrix with actual values of the added columns

squared_data<-squared_data_dup4
start=12*20+1
end=start+24

squared_data[1:ncol(data)-1,start:end]<-as.numeric(t(data[start:end,2:ncol(data)]))

  

write.xlsx2(squared_data,"..\\data\\processed_morocco_MRIO.xlsx",sheetName = "IIOS",row.names = FALSE)